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from transformers import pipeline
import base64
from langchain.chains.summarize import load_summarize_chain
from langchain.docstore.document import Document
from langchain.document_loaders.pdf import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from PyPDF2 import PdfReader
import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
# notes
# https://huggingface.co/docs/transformers/pad_truncation
# file loader and preprocessor
def file_preprocessing(file, skipfirst, skiplast):
loader = PyMuPDFLoader(file)
pages = loader.load_and_split()
print("")
print("# pages[0] ##########")
print("")
print(pages[0])
print("")
print("# pages ##########")
print("")
print(pages)
# skip page(s)
if (skipfirst == 1) & (skiplast == 0):
del pages[0]
elif (skipfirst == 0) & (skiplast == 1):
del pages[-1]
elif (skipfirst == 1) & (skiplast == 1):
del pages[0]
del pages[-1]
else:
pages = pages
print("")
print("# pages after loop ##########")
print("")
print(pages)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, # number of characters
chunk_overlap=100,
length_function=len,
separators=["\n\n", "\n", " ", ""], # default list
)
# https://dev.to/eteimz/understanding-langchains-recursivecharactertextsplitter-2846
texts = text_splitter.split_documents(pages)
final_texts = ""
for text in texts:
final_texts = final_texts + text.page_content
return final_texts
def preproc_count(filepath, skipfirst, skiplast):
input_text = file_preprocessing(filepath, skipfirst, skiplast)
text_length = len(input_text)
return input_text, text_length
def postproc_count(summary):
text_length = len(summary)
return text_length
# llm pipeline
def llm_pipeline(tokenizer, base_model, input_text):
pipe_sum = pipeline(
"summarization",
model=base_model,
tokenizer=tokenizer,
max_length=600,
min_length=300,
truncation=True,
)
result = pipe_sum(input_text)
result = result[0]["summary_text"]
return result
@st.cache_data
# function to display the PDF
def displayPDF(file):
with open(file, "rb") as f:
base64_pdf = base64.b64encode(f.read()).decode("utf-8")
# embed pdf in html
pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
# display file
st.markdown(pdf_display, unsafe_allow_html=True)
# streamlit code
st.set_page_config(layout="wide")
def main():
st.title("RASA: Research Article Summarization App")
uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
if uploaded_file is not None:
st.subheader("Options")
col1, col2, col3 = st.columns([1, 1, 2])
with col1:
model_names = [
"T5-Small",
"BART",
]
selected_model = st.radio("Select a model to use:", model_names)
if selected_model == "BART":
checkpoint = "ccdv/lsg-bart-base-16384-pubmed"
tokenizer = AutoTokenizer.from_pretrained(
checkpoint,
truncation=True,
legacy=False,
model_max_length=1000,
trust_remote_code=True,
)
base_model = AutoModelForSeq2SeqLM.from_pretrained(
checkpoint, torch_dtype=torch.float32, trust_remote_code=True
)
else: # default Flan T5 small
checkpoint = "MBZUAI/LaMini-Flan-T5-77M"
tokenizer = AutoTokenizer.from_pretrained(
checkpoint,
truncation=True,
legacy=False,
model_max_length=1000,
)
base_model = AutoModelForSeq2SeqLM.from_pretrained(
checkpoint, torch_dtype=torch.float32
)
with col2:
st.write("Skip any pages?")
skipfirst = st.checkbox("Skip first page")
skiplast = st.checkbox("Skip last page")
with col3:
st.write("Background information (links open in a new window)")
st.write(
"Model class: [BART](https://huggingface.co/docs/transformers/main/en/model_doc/bart)"
" | Specific model: [MBZUAI/LaMini-Flan-T5-77M](https://huggingface.co/MBZUAI/LaMini-Flan-T5-77M)"
)
st.write(
"Model class: [T5-Small](https://huggingface.co/docs/transformers/main/en/model_doc/t5)"
" | Specific model: [ccdv/lsg-bart-base-16384-pubmed](https://huggingface.co/ccdv/lsg-bart-base-16384-pubmed)"
)
if st.button("Summarize"):
col1, col2 = st.columns(2)
filepath = "data/" + uploaded_file.name
with open(filepath, "wb") as temp_file:
temp_file.write(uploaded_file.read())
with col1:
input_text, text_length = preproc_count(filepath, skipfirst, skiplast)
st.info(
"Uploaded PDF | Number of words: "
f"{text_length:,}"
)
pdf_viewer = displayPDF(filepath)
with col2:
with st.spinner("Please wait..."):
summary = llm_pipeline(tokenizer, base_model, input_text)
text_length = postproc_count(summary)
st.info(
"PDF Summary | Number of words: "
f"{text_length:,}"
)
st.success(summary)
st.markdown(
"""<style>
div[class*="stRadio"] > label > div[data-testid="stMarkdownContainer"] > p {
font-size: 1rem;
font-weight: 400;
}
div[class*="stMarkdown"] > div[data-testid="stMarkdownContainer"] > p {
margin-bottom: -15px;
}
div[class*="stCheckbox"] > label {
margin-bottom: -15px;
}
body > a {
text-decoration: underline;
}
</style>
""",
unsafe_allow_html=True,
)
if __name__ == "__main__":
main()
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